Odds Ratio Analyses

Odds ratios were calculated for each risk factor. The results are separated based on socio-demographic factor and discuss initial distribution of the factor throughout the city using GIS analysis, and odds ratio tests conducted.

Table 4: Odds Ratio of Population Density

Population Density Low (hospital areas) High (hospital areas) Rate of dengue Low (hospital areas) 5 (1,3,6,8) 5 (0,2, 7, 9)

High (hospital areas) 5 (4, 5,10,11) 1 (null)

Odds Ratio 0.2

95% CI 0.017 – 2.39

Table 5: Odds Ratios of Proportion of Residents Born in a Country Bordering Argentina

% People born in country bordering

Argentina

Low (hospital areas) High (hospital areas)

Rate of

Table 6: Odds Ratio of People Living with Unsatisfied Basic Needs (UBN)

Table 7: Odds Ratios of Proportion of Homes Lacking Sanitary Installations

% Homes Lacking

The odds ratios computed above show that the areas with high population density have odds 0.2 times higher to have above-average dengue incidence than areas with low population density. Areas with high proportions of people born in a bordering country have odds 0.47 times higher of having above-average levels of dengue than areas with low immigration levels. Areas with high proportions of people lacking adequate sanitation have odds three times higher of having high levels of dengue than areas with low proportions of people lacking adequate sanitation. Areas with high proportions of people living with UBN have odds nine times higher than areas with low proportions of people living with UBN.

Using the above odds ratio calculations, a summative odds ratio table was constructed (Table 8). This table identifies the risk factors of high dengue, and shows the odds of these high

% People living with

unsatisfied basic needs (UBN)

Low (hospital areas) High (hospital areas)

Rate of dengue Low (hospital areas) 6 (1,2,3,6, 8, 9) 2 (0, 7)

High (hospital areas) 1 3 (5,10,11)

Odds Ratio 9

95% CI 0.5629 –143.90

risk factors with high levels of dengue. To find these, the odds ratio tables were reversed for the original odds ratios of population density and proportion of people born in a bordering country, because here, the data suggest that there is higher risk of dengue with low presence of these risk factors. The calculations done for these new odds ratios are present in Appendix B. However, for the poverty indicators of sanitation and UBN, high levels of dengue are present in areas with the higher risk factors, as is shown in the tables above.

As shown in Table 8, the odds of having dengue are five times higher in hospital areas with low population density, 2.14 times higher in hospital areas with low immigrant populations, five times higher in areas with hospital areas with people lacking adequate sanitation, and five times higher in hospital areas with high proportions of people living with UBN. We then compared the odds ratios with the spatial distribution of cases and dengue, to determine that an environment with high poverty, low migrant populations and low population density in the south of the city is the environment that had the greatest odds for high dengue incidence.

Table 8: Risk Factors for Dengue in Buenos Aires

Risk Factor Odds Ratio 95% CI

Low population density ⁵ 0.42 – 59.66

Low population proportion of immigrants ^2.1429 0.17 – 27.11 High proportion of people lacking adequate sanitation ⁵ 0.22 – 313.86 High proportion of people living with UBN ⁹ 0.35 – 548.99

The confidence intervals for all risk factor analyses are large, due to the small sample size, and prevent these findings from being statistically significant. However, the odds ratios calculated are extreme values using this scale; they all have a large difference from 1.00.

According to effect size, the greater distance the odds ratio is from one suggests greater

significance in the data. The large effect size for all odds ratios suggests that further studies need to be done, but that these factors are related to dengue incidence. ⁴⁷

When conducting the distribution of the risk factors across dengue cases, calculations revealed similar findings. Cases of dengue were:

• 76% more likely to be in areas of low population density;

• 89.4% more likely to come from areas with low proportions of people born in bordering countries;

• 71.5% more likely to come from areas with high proportions of homes lacking adequate sanitation; and

• 67% more likely to come from areas with high proportions of residents with UBN.

Furthermore, the areas of the city with low population density, low immigration rates, and high poverty indicators are generally in the south of the city. Piñero, Penna and Santojanni all exhibit the highest dengue incidence in the city, and low population density. This aggregate level data suggests an association between dengue and these risk factors.

Dengue experts stated that the majority of cases were from immigrants, yet the hospital areas with the highest proportions of immigrants, Argerich and Mejía, did not report high dengue incidence. While lack of dengue reporting is an obvious factor to be considered, these hospital areas are also above the city-average of population density, suggesting that the higher population density in these areas has limited the spread of dengue.

Overall, we see that population density and geographic spread, combined with poverty indicators, appears to be related to dengue trends throughout Buenos Aires. We see that three hospital areas in the south of the city demonstrate low population density, high poverty indicators and low immigrant populations. These hospital areas are Santojanni, Penna and Piñero, located in the south of the city. Though there is a large confidence interval, these findings

suggest that dengue is more prevalent in the sub-urban areas in the southern parts of the city, outside of the ultra-dense city core.